Modeling Multicast Group in Wireless Social Networks: A Combination of Geographic and Non-Geographic Perspective

• Published in: IEEE transactions on wireless communications 2017-06, Vol.16 (6), p.4023-4037
• Main Authors: Liu, Jiaqi, Fu, Luoyi, Zhang, Jinbei, Wang, Xinbing, Xu, Jun
• Format: Article
• Language: English
• Subjects: Analytical models; Communications traffic; Datasets; Group size; Load modeling; Loads (forces); multicast; Multicasting; Random variables; Receivers; Social network services; Social networks; social relationship; Traffic engineering; Traffic flow; Traffic models; Wireless communications; Wireless networks; wireless social networks
• ISSN: 1536-1276; 1558-2248
• DOI: 10.1109/TWC.2017.2691317

Social characteristics have been observed to significantly affect the communications in wireless social networks, especially that within a group following the multicast manner. To model the multicast group in wireless social networks, we should incorporate two important social characteristics, i.e., social relationship and group size. In most existing works, the modeling of social relationship only considers geographic factor. However, such models fail to well characterize wireless social networks, since unlike that in traditional wireless networks, geographic distance is no longer the major factor that affects people's communications and some non-geographic factors, such as user popularity, become more and more important. Moreover, group size is always assumed to be known a priori in previous works, which cannot fully meet the realistic condition. Therefore, in this paper, we model the multicast group from a combination of geographic and non-geographic (GN) perspectives. Specifically, we propose the GN Model to characterize social relationship and the independently-selected model to characterize group size. In addition to the geographic distance considered in the modeling of social relationship, we also introduce user popularity which reflects the influence of each user on others. Then, we assume that the source transmits the data packet to all his friends following the multicast manner. Based on the proposed model, we calculate transmission distance and network traffic load, and then discuss how they are influenced by both geographic and GN factors. Moreover, our proposed models are verified through experimental measurements based on real datasets.